Apoptosis, a precisely regulated and energy-dependent form of cell death, is an important part of neurodevelopment, homeostasis, and neurodegeneration. It is a key component of disparate brain disorders including fetal alcohol syndrome, ischemic stroke, and Parkinson's disease; and it may play a role in behavioral disorders such as schizophrenia, major depression, and posttraumatic stress disorder. Although there are numerous mouse models of neurological and psychiatric disease, there has been no straightforward method for quantifying and locating all of the apoptotic cells in whole mouse brains. We propose to develop and optimize the following technical solution to this problem: A transgenic mouse containing a fluorescent marker of caspase-3 activity - a pivotal executioner enzyme in the apoptosis signaling cascade - will be validated by cell morphological and biochemical assays. This apoptosis reporter mouse will be bred with various disease models, including dopamine transporter knockouts, microRNA-9-deficient mice, and torsin A mutants; the brains of offspring will first be submitted to high-resolution magnetic resonance and diffusion tensor imaging, to elucidate overall structural details and fiber tracts, respectively. After the tissue has been sliced into several 1- to 2-mm-thick sagittal sections and clarified in an aqueous solution, the sections will be scanned completely in three dimensions by two-photon confocal microscopy at a resolution of approximately 1 micron. The two- photon images will then be registered to the magnetic resonance and diffusion tensor images, providing a multimodal view of the entire brain. Automatic segmentation and cell counting algorithms will be used to quantify the number of apoptotic cells in each brain region; and analysis will involve correlating areas of increased or decreased cell death with aberrations in structural morphology and fiber tract anisotropy. The final stage of our method will be to upload the multimodal images of numerous mutant models of disease to a server or cloud in a format that is easily accessible to researchers and the public. We believe that this methodology will be straightforward enough to be pursued by any research group with the appropriate hardware; and that the development of this method will give rise to general tools for the advancement of multimodal imaging of biological phenomena.